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Nguyen N.T.,ISPB | Nguyen N.T.,University of Lyon | Nguyen N.T.,French Institute of Health and Medical Research | Vendrell J.A.,ISPB | And 19 more authors.
Molecular Oncology

We aimed at highlighting the role of ZNF217, a Krüppel-like finger protein, in Estrogen Receptor-α (ERα)-positive (ER+) and luminal breast cancers. Here we report for the first time that ZNF217 and ERα proteins bind to each other in both breast cancer cells and breast tumour samples, via the ERα hinge domain and the ZNF217 C-terminal domain. ZNF217 enhances the recruitment of ERα to its estrogen response elements (ERE) and the ERα-dependent transcription of the GREB1 estrogen-regulated gene. The prognostic power of ZNF217 mRNA expression levels is most discriminatory in breast cancers classified with a "good prognosis", particularly the Luminal-A subclass. A new immunohistochemistry ZNF217 index, based on nuclear and cytoplasmic ZNF217 staining, also allowed the identification of intermediate/poor relapse-free survivors in the Luminal-A subgroup. ZNF217 confers tamoxifen resistance in ER+ breast cancer cells and is a predictor of relapse under endocrine therapy in patients with ER+ breast cancer. ZNF217 thus allows the re-stratification of patients with ER+ breast cancers considered as cancers with good prognosis where no other biomarkers are currently available and widely used. Here we propose a model in ER+ breast cancer where ZNF217-driven aggressiveness incorporates ZNF217 as a positive enhancer of ERα direct genomic activity and where ZNF217 possesses its highest discriminatory prognostic value. © 2014 Federation of European Biochemical Societies. Source

Gyorffy B.,MTA TTK Lendulet Cancer Biomarker Research Group | Gyorffy B.,MTA SE Pediatrics and Nephrology Research Group | Gyorffy B.,Semmelweis University | Hatzis C.,Yale University | And 4 more authors.
Breast Cancer Research

There is growing consensus that multigene prognostic tests provide useful complementary information to tumor size and grade in estrogen receptor (ER)-positive breast cancers. The tests primarily rely on quantification of ER and proliferation-related genes and combine these into multivariate prediction models. Since ER-negative cancers tend to have higher proliferation rates, the prognostic value of current multigene tests in these cancers is limited. First-generation prognostic signatures (Oncotype DX, MammaPrint, Genomic Grade Index) are substantially more accurate to predict recurrence within the first 5 years than in later years. This has become a limitation with the availability of effective extended adjuvant endocrine therapies. Newer tests (Prosigna, EndoPredict, Breast Cancer Index) appear to possess better prognostic value for late recurrences while also remaining predictive of early relapse. Some clinical prediction problems are more difficult to solve than others: there are no clinically useful prognostic signatures for ER-negative cancers, and drug-specific treatment response predictors also remain elusive. Emerging areas of research involve the development of immune gene signatures that carry modest but significant prognostic value independent of proliferation and ER status and represent candidate predictive markers for immune-targeted therapies. Overall metrics of tumor heterogeneity and genome integrity (for example, homologue recombination deficiency score) are emerging as potential new predictive markers for platinum agents. The recent expansion of high-throughput technology platforms including low-cost sequencing of circulating and tumor-derived DNA and RNA and rapid reliable quantification of microRNA offers new opportunities to build extended prediction models across multiplatform data. © Gyorffy et al. Source

Gyorffy B.,MTA TTK Lendulet Cancer Biomarker Research Group | Gyorffy B.,Semmelweis University | Gyorffy B.,MTA SE Pediatrics and Nephrology Research Group | Karn T.,Goethe University Frankfurt | And 4 more authors.
International Journal of Cancer

The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n=3,534, HR=3.68, p=1.67 E256). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n=427, HR=3.08, p=0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR=3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q5Retraining. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers. © 2014 UICC. Source

Deng L.,University of Sichuan | Gyorffy B.,MTA TTK Lendulet Cancer Biomarker Research Group | Gyorffy B.,Semmelweis University | Gyorffy B.,MTA SE Pediatrics and Nephrology Research Group | And 6 more authors.
Journal of Thoracic Oncology

Introduction: Immune checkpoint blockade is being investigated in clinical trials and showed great potential in lung cancer. The prognostic roles of and clinicopathological factors associated with immune checkpoint gene expression, CTLA-4 and PDCD1 remain largely undefined, which encodes cytotoxic-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1), respectively. Methods: We used a lung cancer database of 1715 patients measured by Affymetrix microarrays to analyze the association of gene expression with clinicopathological factors and survival. Hazard ratio (HR) and 95% confidence interval (CI) for overall survival (OS) were calculated. Cutoffs were determined by median across the entire database. Results: In 909 patients with histology information, significantly higher PDCD1 and CTLA-4 expression were found in squamous carcinoma than adenocarcinoma. In 848 patients with known smoking history, current/former smokers were found to have significantly elevated gene expression compared with nonsmokers. Significant higher expression of both genes were found in TNM stage II versus I. Higher expression of PDCD1 predicted worse OS in univariate analysis, but not in multivariate (HR: 1.22; 95% CI: 0.53-2.79). CTLA-4 was marginally significant in univariate analysis of the entire set (HR: 1.15; 95% CI: 0.99-1.34). In patients with information for multivariate analysis, higher expression of CTLA-4 was associated with worse OS (HR: 1.96; 95% CI: 1.18-3.31). Conclusions: In this study with large number of patients, PDCD1 and CTLA-4 expression is significantly higher in squamous carcinoma and current/former smokers. Higher expression of CTLA-4, but not PDCD1 predicts worse survival. © 2015 by the International Association for the Study of Lung Cancer. Source

Menyhart O.,MTA TTK Lendulet Cancer Biomarker Research Group | Santarpia L.,IRCCS Humanitas Clinical and Research Institute | Gyorffy B.,MTA TTK Lendulet Cancer Biomarker Research Group | Gyorffy B.,MTA SE Pediatrics and Nephrology Research Group | Gyorffy B.,Semmelweis University
Current Cancer Drug Targets

The introduction of trastuzumab for anti-HER2 therapy dramatically changed the clinical outcome for HER2 (ERBB2, neu) positive breast cancer patients. Today, patients eligible for trastuzumab are selected using HER2 expression/amplification status of the primary tumor. However, acquired and inherent resistance to anti-HER2 therapy in these patients poses a significant challenge, and better patient stratification will be needed to improve clinical response. Here, we provide a wide-ranging overview of potential biomarkers capable of stratifying patients regarding their response to trastuzumab. These include HER2 amplification, impaired access to the binding site (p95HER2, Δ16HER-2, MUC4), augmented signaling through other ERBB family receptors (HER1, HER3, HER4) and their ligands, activation of HER2 targets by alternate heterodimers (EphA2, IGF-1R, GDF15, MUC1*), signaling triggered by downstream members (PIK3CA, PTEN, SRC, mTOR), altered expression of cell cycle and apoptotic regulators (CDKs, p27kip1, Bcl-2), hormone receptor status, resistance to antibody-dependent cellular cytotoxicity (FcγR), and altered miRNA expression signatures. Multigenic molecular profile analyses have revealed further genes not directly associated with classical oncogenic pathways. Although numerous biomarkers have shown promise in pre-clinical studies, many have delivered controversial results when evaluated in clinical trials. One of the keys for targeting ERBB2 will be to consider the entire ERBB family and downstream associated pathways responsible for the malignant transformation. The heterogeneity of the disease is likely to represent a significant obstacle to accurately predicting the course of resistance. The future most probably involves the incorporation of multiple biomarkers into a unified predictor enabling selection of patients for superior targeted drug administration. © 2015 Bentham Science Publishers. Source

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